1 intro

Purpose:

Validate runwise ub55 GLMs in terms of marginal HRFs (averaged across conditions) and high-vs-low contrast.
Define target TRs for each task.
Calculate HiLo conjunction effect, and create list of conjunction parcels.

Notes on analyses:

GLMs:

DMCC55B
run-wise, 1trpk, shifted TENTs
Cuedts: terms for \(\text{trial-type (congr, incon)}\times\text{sequence (switch, repeat)}\) instead of \(\text{incentive}\)

Target TRs:

Axcpt: 7, 8, 9
Cuedts: 9, 10
Stern: 11, 12
Stroop: 2, 3, 4
Axcpt, Cuedts, and Stern begins at roughly ~4 TRs post-cue onset, and ending at ~8 TRs post-cue onset
Stroop begins at 2 TRs post-target onset, ends at 6 TRs post-target onset

Contrasts (on regional means):

Axcpt: \(\text{BY} - \text{BX}\)
Cuedts: \((\text{InConSwitch} + \text{InConRepeat} - \text{ConSwitch} - \text{ConRepeat})/2\)
Stern: \(\text{LL5RN} - \text{LL5NN}\)
Stroop: \((\text{PC50InCon} + \text{biasInCon} - \text{PC50Con} - \text{biasCon})/2\)

Plotting and statistical details:

within ‘curves’ sections, ribbons indicate 95% CI of bootstrapped mean beta across subjects (one ribbon per run).
all p-value corrections were performed whole-cortex, with FDR.
for “stats and maps” sections, HLM was used to estimate t-statistics on the hi-lo contrast per ROI:
- random-intercept models
- level-I: (TR within target window)*(run)
- level-2: subject
- \(\beta_{trs} = b_0 + d_s + \epsilon_{trs}\)
  - \(\beta_{trs}\) is high-low contrast beta estimate
  - \(t, r, s\) are indices for TR, run, and subject
  - \(d\) is is subject-specific intercept
  - \(\epsilon\) is error

2 Network-level curves

Estimates are first averaged across parcels within network per subject. Group-level stats and plots are then displayed at the network-level.

2.1 marginal beta estimates (averaged across all trialtypes)

2.2 conditional (per trialtype)

2.3 contrasts

2.3.1 beta estimates

2.3.2 t-statistics

3 Parcel-level curves

3.1 counts of significant effects per TR

Using t-test of subject parcel means, averaged across run. Negative counts show deactivations.

3.2 DMCC 34: contrast curves per task

3.2.1 beta estimates

3.2.2 t-statistics

3.3 Cross-task conjunction (“DMCC55B parcels”)

3.3.1 Parcels ID’d

Here conjunction analysis is performed by feeding TR-level contrast estimates to an HLM (see description at beginning of doc).

Number of parcels identified, and parcel names:

task	n.activated
Axcpt	123
Cuedts	108
Stern	34
Stroop	176

##  [1] "LH_Cont_Par_4"              "LH_Cont_PFCl_6"            
##  [3] "LH_Cont_PFCl_7"             "LH_Cont_PFCmp_1"           
##  [5] "LH_Cont_PFCv_1"             "LH_Default_PFC_10"         
##  [7] "LH_Default_PFC_20"          "LH_Default_PFC_24"         
##  [9] "LH_DorsAttn_Post_10"        "LH_DorsAttn_Post_9"        
## [11] "LH_SalVentAttn_FrOperIns_3" "LH_SalVentAttn_FrOperIns_5"
## [13] "LH_SalVentAttn_FrOperIns_7" "LH_SalVentAttn_FrOperIns_9"
## [15] "LH_SalVentAttn_Med_4"       "LH_Vis_21"                 
## [17] "RH_Cont_Par_6"              "RH_Cont_PFCl_10"           
## [19] "RH_Cont_PFCl_12"            "RH_Cont_PFCl_13"           
## [21] "RH_Cont_PFCl_7"             "RH_Cont_PFCl_9"            
## [23] "RH_Cont_PFCmp_1"            "RH_Cont_PFCmp_2"           
## [25] "RH_Cont_PFCv_1"             "RH_Default_PFCdPFCm_9"     
## [27] "RH_Default_PFCv_4"          "RH_SalVentAttn_FrOperIns_5"
## [29] "RH_SalVentAttn_Med_4"

3.3.2 Contrast curves per task

3.3.2.1 beta estimates

3.3.2.2 t-statistics

4 Brains

Target TRs
t-values displayed; from HLM fitted to TR-level data (see intro)
colors are reversed (black = high, yellow = low) so large positive effects can be seen on white underlay.

UB55 univariate analysis: hi-lo contrast, group-level

michael freund

2020-11-06